Enhancing Zero-Shot Cross-Lingual Event Argument Extraction with Language-Independent Information

Abstract

In previous research on zero-shot cross-lingual event argument extraction (EAE), almost no one has considered constructing models that utilize language-independent information to promote cross-lingual transfer and improve model performance. In the paper, building on previous studies, we take them a step further. Our approach encodes structures of the event and gets the relevance among argument roles by regarding EAE as a language generation problem and introducing language-independent information of event argument roles to the model’s input. In order to facilitate cross-lingual transfer, we also introduce language-independent templates to express structures of the event argument that are consistent with all languages. Our suggested approach fine-tunes multilingual pre-trained generative language models to extract event arguments from the input passages (the process of extracting these arguments combines the role relevance learned from the input language-independent role information), which fills language-independent templates and ultimately generates the required string sentences. After training on source languages, the model is applied immediately to target languages in EAE. Extensive experiments on two datasets which contain English, Arabic and Spanish show that our proposed approach results in a substantial improvement compared with previous studies on zero-shot cross-lingual EAE. Additional analysis illustrates the interpretability of our method.